In this weeks lab we learn about the block bootstrap. A non parametric way to deal with spatial auto correlation in your data and still make valid inferences.
•Bootstrapping allows us to find the unknown distribution of a statistic by resampling the original data (with replacement) and recalculating the statistic many times.
•Hence we can calculate p-values and standard errors of things we don’t know the distribution of.
•Assumptions: observations are independent and identically distributed ("iid")